A Visual Learning based Robotic Grasping System

Weijun Guan, Yulan Guo
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引用次数: 1

Abstract

Deep learning has promoted the development of many areas in computer vision and robotics. However, most of the researches focus on an individual task. In this paper, we design a multi-task robot system based on ROS platform and YOLO network to complete the object detection, positioning, and grasping tasks. In terms of hardware, a heterogeneous computing platform is established to achieve high computing power while reducing energy consumption. In terms of software, an algorithm framework is designed for the multi-task robot system according to the characters the heterogeneous computing platform. Experimental results on real data show that the proposed robot system achieves promising object detection, positioning and grasping performance.
基于视觉学习的机器人抓取系统
深度学习促进了计算机视觉和机器人技术许多领域的发展。然而,大多数研究都集中在单个任务上。在本文中,我们设计了一个基于ROS平台和YOLO网络的多任务机器人系统来完成目标检测、定位和抓取任务。硬件方面,建立异构计算平台,实现高计算能力的同时降低能耗。软件方面,根据异构计算平台的特点,设计了多任务机器人系统的算法框架。实际数据的实验结果表明,该机器人系统具有良好的目标检测、定位和抓取性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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